sdatip 1.0.1

Fast stochastic determination of arrival time and initial polarity of seismic waveforms

SDATIP

Fast Stochastic Determination of Arrival Time and Initial Polarity of Seismic Waveforms

A high-performance Python package for seismic waveform analysis that determines arrival time and initial polarity using Markov chain-based stochastic methods.

Performance

Metric	Original	SDATIP
100Hz, 10s waveform	~30 minutes	~55 seconds
Speedup	-	30x faster

Installation

pip install sdatip

Or install from source:

git clone https://github.com/Chuan1937/SDATIP-Fast.git
cd SDATIP-Fast
pip install -e .

Quick Start

Process a Single Waveform

import numpy as np
import sdatip

# Your waveform data (1D numpy array)
data = np.random.randn(1000)

# Process the waveform
result = sdatip.process_waveform(
    name="STATION_A",
    data=data,
    output_dir="./output/"
)

# Get results
print(f"Arrival time: {result['results'][0]['arrival_time']:.3f}s")
print(f"Polarity (up): {result['results'][0]['polarity_up']:.3f}")

Process Multiple Waveforms

import sdatip

# Batch process all SAC files in a directory
results = sdatip.process_batch(
    input_dir="./data/",
    output_dir="./output/",
    num_workers=4,  # Use 4 CPU cores
    plot_enabled=False
)

Step-by-Step Processing

import sdatip

# Create waveform object
wf = sdatip.Waveform("station")
wf.importdata(data, delta=0.01)
wf.analyzedata()
wf.rmmean()
wf.interpolate(1)
wf.denseunique()
wf.denselong(hvcoefficient=2.5, mininsertco=200)
wf.extremearr()
wf.densebin()

# Build state model
state = wf.constructstate()
timeprobs, num_solutions = state.markovmatrix()
ampprob = state.ampprobcalculate()

# Estimate results
state.estimation(0)
print(f"Arrival: {state.arrivalestimate:.3f}s")
print(f"Polarity up: {state.polarityup:.3f}")

API Reference

process_waveform(name, data, output_dir, ...)

Process a single seismic waveform.

Parameter	Type	Default	Description
name	str	required	Station name
data	np.ndarray	required	1D amplitude array
output_dir	str	required	Output directory
delta	float	0.01	Sampling interval (s)
plot_enabled	bool	True	Generate plots

process_batch(input_dir, output_dir, ...)

Process multiple waveforms in parallel.

Parameter	Type	Default	Description
input_dir	str	required	Input directory with SAC files
output_dir	str	required	Output directory
num_workers	int	-1	CPU cores (-1 = all)
plot_enabled	bool	False	Generate plots

Classes

• Waveform(name) - Waveform preprocessing container
• State(name) - Markov state model

Output Files

File	Description
{station}.txt	Text summary with arrival time and polarity
{station}.npz	Main results (matrix, probabilities, thresholds)
{station}_timeprob_{i}.npz	Time probability distribution
{station}_{i}.pdf	Probability plot (if enabled)
{station}.eps	Publication-ready plot (if enabled)

Algorithm

Based on: Pei, W., Zhuang, J. & Zhou, S. "Stochastic determination of arrival time and initial polarity of seismic waveform." Earth Planets Space 77, 36 (2025). https://doi.org/10.1186/s40623-025-02161-5

Validation

python benchmark_validate.py check

Citation

@article{pei2025stochastic,
  title={Stochastic determination of arrival time and initial polarity of seismic waveform},
  author={Pei, W. and Zhuang, J. and Zhou, S.},
  journal={Earth, Planets and Space},
  volume={77},
  pages={36},
  year={2025},
  doi={10.1186/s40623-025-02161-5}
}

License

MIT License

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